# Graspy: A python package for rigorous statistical analysis of populations of attributed connectomes

> **NIH NIH RF1** · JOHNS HOPKINS UNIVERSITY · 2020 · $1,246,005

## Abstract

PROJECT SUMMARY
Overview: We will extend and develop implementations of foundational methods for analyzing populations of
attributed connectomes. Our toolbox will enable brain scientists to (1) infer latent structure from individual
connectomes, (2) identify meaningful clusters among populations of connectomes, and (3) detect relationships
between connectomes and multivariate phenotypes. The methods we develop and extend will naturally
overcome the challenges inherent in connectomics: high-dimensional non-Euclidean data with multi-level
nonlinear interactions. Our implementations will comply with the highest open-source standards by: providing
extensive online documentation and extended tutorials, hosting workshops to demonstrate our tools on an
annual basis, and merging our implementations into commonly used packages such as scikit-learn [1], scipy
[2], and networkx [3].
All of the code we develop is open source. We strive to ensure that our code is shared in accordance with the
strictest guiding principles. We chose to implement these algorithms in Python due to its wide adoption in the
neuroscience and data science fields. In particular, many other neuroscience tools applicable to connectomics,
including NetworkX DiPy, mindboggle, nilearn, and nipy, are also implemented in Python. This will enable
researchers to chain our analysis tools onto pre-existing pipelines for data preprocessing and visualization.
Nonetheless, we feel that sharing our code in our own public repositories is insufficient for global reach. We
have also begun reaching out to developers of the leading data science packages in python, including scipy,
sklearn, networkx, scikit-image, and DiPy. For each of those packages, we have informal approval to begin
integrating algorithms that we have developed. Those packages are collectively used by >220,000 other
packages, so merging our algorithms into those packages will significantly extend our global reach.
All researchers investigating connectomics, including all the authors of the 24,000 papers that mention the
word “connectome”, will be able to apply state-of-the-art statistical theory and methods to their data. Currently,
we have about 150 open source software projects on our NeuroData GitHub organization. Collectively, these
projects get about 2,000 downloads and >11,000 views per month. As we incorporate additional functionality
as described in this proposal, we expect far more researchers across disciplines and sectors will utilize our
software.
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## Key facts

- **NIH application ID:** 10012519
- **Project number:** 1RF1MH123233-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Carey Priebe
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,246,005
- **Award type:** 1
- **Project period:** 2020-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10012519

## Citation

> US National Institutes of Health, RePORTER application 10012519, Graspy: A python package for rigorous statistical analysis of populations of attributed connectomes (1RF1MH123233-01). Retrieved via MORVS 2026-10-27 from https://api.morvs.ai/grant/nih/10012519. Licensed CC0.

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